Rich and comprehensive knowledge graphs (KG) of the Web, such as, Google KG, NELL, and Diffbot KG, are becoming increasingly prevalent and powerful as the underlying AI technology is rapidly progressing. In this work, we leverage this ongoing advancement for the task of answering questions posed from any domain and any type (factoid and non-factoid). We present a framework for knowledge graph based question answering systems, KGQA, and experiment with two instances of this framework that employ Diffbot and Google KG.
Computers have become so common in our daily life, and there has been an increase in demand for Human Computer Interaction. Gesture recognition is an emerging technology for Human Computer Interaction and has a wide variety of applications like gaming and virtual reality. Previous work and methods on finger gesture recognition either use sensors that are worn by users or cameras that require proper light to identify gestures.
Variable importance and interaction measures are crucial to breaking open the "black box” of machine-learned classifiers. The existing metrics, however, are data-driven and lack a solid mathematical foundation, resulting in misleading conclusions on certain types of data. We propose feature power: a new variable importance measure based on the Shapley value of cooperative game theory. We evaluate the validity of this new measure and the behavior of feature power in comparison to existing variable importance metrics.